Wren etc. proposed a method based on decomposing the temporal signals at each pixel using digital fourier transform or digital cosine transform to extract periodicity information from the underlying spurious motions. See, Wren, C. R.; Porikli, F., “Waviz: Spectral Similarity for Object Detection”, IEEE International Workshop on Performance Evaluation of Tracking & Surveillance, January 2005 and Porikli, F.; Wren, C. R., “Change Detection by Frequency Decomposition: Wave-Back”, Workshop on Image Analysis for Multimedia Interactive Services, April 2005. If the current frequency signatures are quite different from a model modeling the background frequency signatures, the current pixel is classified as a foreground pixel. These two methods are actually a one-step motion detection method while our filter is designed to smooth out the spurious motion only. One dilemma these two methods face is that they require a fixed window size for the DFT or DCT function. Choosing a wide window gives better frequency resolution but poor time resolution. A narrower window gives good time resolution but poor frequency resolution. A wide window might cause long tails after the moving objects. The tails are caused by background pixels being incorrectly identified as moving pixels due to the “contamination” in the background model from pixels on the moving objects. Wren suggested to alleviate the problem by adjusting window size dynamically.
In contrast, the present invention is more adaptive in this aspect. This is because the filter strength is proportional to the spurious motion strength, and assuming regular moving objects trigger weaker spurious motion responses, which is true for most scenarios, regular moving objects will be subject to a smaller filter strength, which translates into equivalently reducing the window size of the filter. Thus, the implementation in accordance with the present invention is simpler.
There is also active research on salient motion, which detects “salience” of motion in order to separate un-salient spurious motions from salient regular motions. See, R. P. Wildes & L. Wixson, “Detecting Salient Motion Using Spatiotemporal filters and Optical Flow,” Proceedings of the DARPA Image Understanding Workshop, 349-356, 1998. Obviously these approaches are totally different than the present invention.
Salient motion is based on optic flow technique, which is widely regarded as inaccurate and error-prone. If a pedestrian walks 3 steps forward and 2 steps backward, it is very difficult for it being regarded as salient motion. Because the approach of the present invention uses a much longer time period (comparing with typical motions) to detect spurious motion, it will be easier to detect a pedestrian. However, salient motion may hold an advantage in detecting objects in extremely noisy spurious motion areas since the approach of the present invention tends to use very strong filtering which might affect detecting regular moving objects.
So far there is no commercial application for salient motion technique to the author's best knowledge.
Accordingly, new and improved methods and systems for providing spurious filtering in surveillance systems are required.